The Study on the effect of mercury pollution on soil microorganisms around mercury mining area

doi:10.21203/rs.3.rs-3119174/v1

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The Study on the effect of mercury pollution on soil microorganisms around mercury mining area

https://doi.org/10.21203/rs.3.rs-3119174/v1

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published 07 Dec, 2023

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In order to further explore the effects of soil mercury pollution at different distances from the mercury mining area on soil microbial diversity and community structure, 2m, 20m, 30m, 500m and 650m from the periphery of the mercury mining area were selected as soil sampling areas. Four different points were randomly selected from the soil sampling areas of 2m, 20m, 30m, 500m and 650m to collect 0-20cm soil samples under the surface, a total of 20 soil samples. Soil microbial DNA was extracted from 20 soil samples after treatment, and then soil microbial DNA was sequenced by high-throughput sequencing technology. The results showed that in the three levels of order, family and genus, the number of bacteria and fungi in the high mercury soil of 2–30 m from the mercury mining area was significantly higher than that in the high mercury soil of 500–650 m from the mercury mining area. At the level of family and genus, the number of unique bacteria and fungi to soil samples at different points at the same distance was quite different. The results of the effect on the microbial community structure showed that at the order level, the dominant bacteria in the high mercury soil or medium and high mercury soil at 2m, 20m, 30m, 500m, 650m from the mercury mining area were Sphingomonadales and Sphingobacteriales, and the dominant fungi were Agaricales and Sebacinales. At the family level, the dominant bacteria were Sphingomonadaceae and Chitinophagaceae, and the dominant fungi were Sebacinaceae and Hygrophoraceae. At the genus level, the dominant bacteria were norank_Acidobacteria_Gp6, Sphingomonas, norank_Acidobacteria_Gp4, and the dominant fungi were Hygrocybe and Sebacina. The internal heterogeneity of soil caused significant differences in unique bacteria and fungi at the same site, and the emergence of dominant bacteria and fungi was a manifestation of high adaptability to long-term mercury stress and other stresses in soil, which provided a scientific reference for further exploring the mechanism of synergistic mercury enrichment between microorganisms and plants.

Biological sciences/Microbiology

Earth and environmental sciences/Ecology

Earth and environmental sciences/Environmental sciences

Mercury stress

mercury mining area

microorganisms

bacteria

fungi

Soil is an important link between water and atmosphere for material circulation. Soil carries about 90% of pollutants in the environment. Mercury is one of the main control pollutants in 129 priority control pollutants ^1,2,3. Mercury in soil is often enriched and transferred through crops, and then enriched and transferred to animals and humans through various food chains, resulting in serious ecological problems^4,5,which is a major ecological and environmental problem.Therefore, it is particularly urgent to control soil heavy metal pollution. In recent years, researchers in this field have innovated research ideas to explore the use of soil microorganisms and host plants to reduce soil heavy metal content, so as to achieve the purpose of controlling soil heavy metal pollution. Compared with the traditional soil mercury pollution remediation methods such as physical and chemical remediation technology⁶, agricultural engineering technology⁷, phytoremediation technology⁸, the synergistic remediation technology of soil microorganisms and host plants has the advantages of low cost, high efficiency, easy operation, less secondary pollution, air purification and environmental beautification. Soil microorganisms and host plants make full use of plants to fix, extract and volatilize mercury in soil to reduce soil mercury pollution through the synergistic effect of bacteria and plants. This remediation technology has great potential application value and has become one of the hot spots of heavy metal contaminated soil remediation. Soil bacteria, fungi and actinomycetes are the main components of the microflora in the soil ecosystem. When the soil is subjected to heavy metal stress, the soil microflora can reflect the change of soil environmental quality, so it can be used as one of the important biological indicators, and bacteria are more sensitive to heavy metal pollution than fungi⁹. There have been many reports on the effects of mercury pollution on plants, and some plants with strong resistance to mercury stress have been found ^10,11, while there are few reports on the effects of mercury pollution on soil microorganisms ^12,13. Harris-Hellal et al.showed that when the mercury concentration was low (0 ~ 1mg/kg), the microbial community in the forest soil did not change significantly. When the mercury concentration increased to 20mg/kg, the diversity and genetic structure of the soil microbial community changed significantly¹⁴. The combined pollution of heavy metals cadmium and mercury can reduce the diversity of soil microbial communities, and the abundance ratio of Gram-positive bacteria to Gram-negative bacteria increases with the increase of pollution concentration^15,16. Mercury in soil inhibits the growth of fungi, actinomycetes and bacteria, and seriously affects soil bacterial activity and bacterial community structure. In high-concentration mercury-contaminated environments, Gemmatimonadetes were still active but Nitrospirae was declining. Fungi was prone to resistance in high-concentration mercury-contaminated environments^17,18. Frossard A et al.found that when the mercury concentration reached 32mg/kg, it seriously affected the community structure, composition and diversity of bacteria and fungi in forest soil¹⁹. At present, there are few reports on the effects of mercury stress on soil microorganisms, and the relative analysis is not deep enough. Based on this, this experiment was carried out to collected soil samples at different distances from the abandoned mercury mining area in Wanshan, Tongren, Guizhou Province,China. At the same time, high-throughput sequencing was used to explore the effects of different concentrations of mercury in soils at different distances from the mercury mining area on soil microbial diversity and community structure, and screened out some microbial dominant strains with strong mercury tolerance, which provided theoretical support for further exploration of the synergistic mercury enrichment mechanism of mercury-resistant bacteria and plants.

Sample collection

Five sampling points were selected at different distances around the abandoned area of Wanshan Mercury Mine in Tongren, Guizhou Province,China. The first group of soil samples, numbered MSO1, MSO2, MSO3 and MSO4, were collected at different points about 2 m from the sewage outlet of the mercury mining area. The second group of soil samples, numbered CMSO21, CMSO22, CMSO23, CMSO24, were collected from the wasteland of perennial ryegrass about 20 m away from the mining area. The third group of soil samples, numbered GMSO301, GMSO302, GMSO303, GMSO304, were collected from the wasteland of perennial ryegrass growing about 30 m away from the mining area. The fourth group of soil samples, numbered GMSO500, GMSO501, GMSO502, GMSO503 were collected from new cultivated land at different points about 500 m away from the mining area. The fifth group of soil samples, numbered GMSO651, GMSO652, GMSO653 and GMSO654 were collected from long-term cultivated land at different points about 650 m away from the mining area.

All soil samples were taken from 0–20 cm soil under the surface, removed impurities, crushed, and sieved by 1 mm. A total of 20 soil samples were tested. In order to facilitate statistical analysis, all 20 soil samples were grouped. MSO1, MSO2, MSO3, MSO4, CMSO21, CMSO22, CMSO23, CMSO24, GMSO301, GMSO302, GMSO303, GMSO304 soil samples were in the range of 2–30 m from the sewage outlet of the mining area, and these soil samples were classified into the proximal group (CR). Similarly, GMSO500, GMSO501, GMSO502, GMSO503, GMSO651, GMSO652,GMSO653 and GMSO654 soil samples were collected 500 ~ 650 m away from the sewage outlet of the mining area, and these soil samples were classified into the distal group (FBR). Two samples were prepared for all samples in this treatment, one for the determination of soil physical and chemical properties, and one for the determination of soil microbial DNA. The basic physical and chemical properties of soil were shown in Table 1.

Table 1

Physiochemical properties of the tested soil
Soil Number		Distance from the mine mouth(m)	Available N (mg/kg)	Available P(mg/kg)	Available K(mg/kg)	pH	Hg²⁺(mg/kg)
T0	MSO1	0.13	9.06	11.14	29.21	7.5	147.04
	MSO2	0.16	9.11	11.02	30.06	7.5	139.23
	MSO3	0.18	8.98	11.08	30.14	7.5	119.51
	MSO4	0.22	9.02	10.99	27.54	7.5	156.03
T20	CMSO1	21.2	8.07	10.34	27.24	8.0	167.14
	CMSO2	20.10	8.01	10.13	27.31	8.0	139.25
	CMSO3	19.60	7.94	10.22	28.07	8.0	128.33
	CMSO4	20.32	8.11	11.04	28.16	8.5	175.02
T30	GMSO301	30.50	8.97	12.03	30.04	7.5	69.15
	GMSO301	30.20	9.07	12.10	30.12	8.0	72.31
	GMSO301	29.80	8.31	11.43	29.36	7.5	47.29
	GMSO301	30.10	8.16	11.19	27.62	7.5	65.18
T500	GMSO500	500.20	8.09	10.21	30.02	8.5	61.51
	GMSO501	500	8.21	10.32	28.22	8.5	45.42
	GMSO502	500.10	8.15	11.03	28.31	8.5	73.06
	GMSO503	499.80	9.02	11.11	30.07	8.5	59.10
T650	GMSO651	650.30	9.11	11.39	29.11	6.5	96.32
	GMSO652	650.20	8.98	12.01	28.52	7.0	112.04
	GMSO653	650.40	9.05	12.14	29.19	6.5	125.27
	GMSO654	650.10	8.37	12.06	27.45	7.0	104.13

DNA extraction

Total community genomic DNA extraction was performed using a E.Z.N.A™ Mag-Bind Soil DNA Kit (Omega,M5635-02,USA),following the manufacturer's instructions. The concentration of the DNA was measured by using a Qubit 4.0 (Thermo,USA) to ensure that adequate amounts of high-quality genomic DNA had been extracted.

16S rRNA gene amplification by PCR

Our target was the V3-V4 hypervariable region of the bacterial 16S rRNA gene. PCR was started immediately after the DNA was extracted. The 16S rRNA V3-V4 amplicon was amplified using 2×Hieff® Robust PCR Master Mix (Yeasen,10105ES03,China).Two universal bacterial 16S rRNA gene amplicon PCR primers (PAGE purified) were used: the amplicon PCR forward primer (CCTACGGGNGGCWGCAG) and amplicon PCR reverse primer (GACTACHVGGGTATCTAATCC). The reaction was set up as follows: microbial DNA(10 ng/µl) 2µl; amplicon PCR forward primer (10 µM)1µl; amplicon PCR reverse primer (10 µM)1µl;2xHieff® Robust PCR Master Mix (Yeasen,10105ES03,China)(total 30µl).The plate was sealed and PCR performed in a thermal instrument (Applied Biosystems9700,USA) using the following program:1 cycle of denaturing at 95°C for 3 min,first 5 cycles of denaturing at 95°C for 30 s, annealing at 45°C for 30 s,elongation at 72°C for 30 s, then 20 cycles of denaturing at 95°C for 30 s, annealing at 55°C for 30 s, elongation at 72°C for 30 s and a final extension at 72°C for 5 min. The PCR products were checked using electrophoresis in 2% (w/v) agarose gels in TBE buffer (Tris,boricacid,EDTA) stained with ethidium bromide (EB) and visualized under UV light.

16S gene library construction,quantification,and sequencing

Hieff NGSTM DNA Selection Beads (Yeasen,10105ES03,China) were used to purify the free primers and primer dimer species in the amplicon product. Samples were delivered to Sangon BioTech (shanghai) for library construction using universal lllumina adaptor and index. Before sequencing, the DNA concentration of each PCR product was determined using a Qubit® 4.0 Green double-stranded DNA assay and it was quality controlled using a bioanalyzer (Agilent 2100,USA). Depending on coverage needs, all libraries can be pooled for one run. The amplicons from each reaction mixture were pooled in equimolar ratios based on their concentration. Sequencing was performed using the lllumina MiSeq system (lllumina MiSeq, USA), according to the manufacturer's instructions.

Sequence processing,OTU clustering,Representative tags alignment and Biological classification

After sequencing,The two short lllumina readings were assembled by PEAR software (version 0.9.8) according to the overlap and fastq files were processed to generate individual fasta and qual files, which could then be analyzed by standard methods.The effective tags were clustered into operational taxonomic units (OTUs) of ≥ 97% similarity using Usearch software (version 11.0.667). Chimeric sequences and singleton OTUs (with only one read) were removed, after which the remaining sequences were sorted into each sample based on the OTUs. The tag sequence with the highest abundance was selected as a representative sequence within each cluster. Bacterial and fungal OTU representative sequences were classified taxonomically by blasting against the RDP Database and UNITE fungal ITS Database, respectively.

The distribution of soil bacteria and fungi at the order level at different distances from mercury mining areas

At the order level, the distribution of bacteria in the 12 soil samples of the proximal group (CR) was shown in Fig. 1.1. Among them, the number of common bacteria in 12 soil samples of the proximal group (CR) was 72, and the number of unique bacteria in the 12 soil samples was different. Among them, the number of unique bacteria in MSO1 soil samples was 5, only 1unique bacteria in MSO3 and CMSO23 soil samples, no unique bacteria in the other 9 soil samples. The bacterial distribution of 8 soil samples in the distal group (FBR) was shown in Figure.1.2. The number of common bacteria in the 8 soil samples of the distal group (FBR) was 56, of which 10 unique bacteria in the GMSO651 soil sample, 3 unique bacteria in the GMSO503 soil sample, 2 unique bacteria in the GMSO653 soil sample, and 1unique bacteria in the GMSO500 soil sample. The results of bacterial distribution of all 20 soil samples showed that the number of bacteria in each sample of 12 soil samples in the proximal group (CR) was about 72, and in each sample of 8 soil samples in the distal treatment group (FBR) was about 58. The number of bacteria in the high-mercury soil near the mercury mining area was higher than that in the medium-high-mercury soil far from the mercury mining area.

At the order level, the distribution of fungi in 12 soil samples of the proximal group (CR) was shown in Fig. 1.3. The total number of fungi in all 12 soil samples was 30, but the number of unique fungi to each of the 12 soil samples was different. Among them, the number of unique fungi in MSO3 soil samples was 3. 2 unique fungi in CMSO21, CMSO23, CMSO24, GMSO302, GMSO303 soil samples, 1unique fungi in MSO2 and MSO4 soil samples, and 0 unique fungi in MSO1, CMSO22, GMSO301, GMSO304 soil samples. The distribution of fungi in 8 soil samples of the distal group (FBR) was shown in Fig. 1.4. The number of common fungi in 8 soil samples was 19, of which 9 unique fungi in GMSO501 soil samples, 5 unique fungi in GMSO503 soil samples, 3 unique fungi in GMSO654 soil samples, 1 unique fungi in GMSO500 and GMSO651 soil samples, and 0 unique fungi in GMSO652 and GMSO653 soil samples. From the analysis of the number of fungi in all 20 soil samples, the number of fungi per sample in the 12 soil samples of the proximal group (CR) was about 31, and the number of fungi per soil sample in the 8 soil samples of the distal group (FBR) was about 22. The number of fungi in the high-mercury soil near the mercury mining area was higher than that in the medium-high-mercury soil far from the mercury mining area. At the order level, the total number of bacterial in both the proximal group and the distal group was higher than the total number of fungi.

The distribution of soil bacteria and fungi at family level at different distances from mercury mining area

At the family level, the bacterial distribution of 12 soil samples in the proximal group (CR) was shown in Fig. 1.5. The total number of bacteria in all 12 soil samples was 114, and the number of unique bacteria to each of the 12 soil samples was very different. Among them, the number of unique bacteria in MSO1 soil samples was 10. 1 unique bacteria in MSO2, MSO3, MSO4, CMSO21, CMSO22, CMSO23 and CMSO24 soil samples, while 0 unique bacteria in GMSO301, GMSO302, GMSO303 and GMSO304 soil samples. The distribution of bacteria in 8 soil samples of the distal group (FBR) was shown in Fig. 1.6.There were 89 bacteria in the 8 soil samples of the distal group (FBR), and the number of unique bacteria in 8 soil samples was quite different. Among them, the number of unique bacteria in GMSO651 soil samples was 22, 7 unique bacteria inGMSO653 soil samples, 3 unique bacteria in GMSO500, GMSO503 and GMSO652 soil samples, 1unique bacteria in GMSO502 soil samples, 0 unique bacteria in GMSO501 and GMSO654 soil samples. From the analysis of the number of bacteria in all 20 soil samples, the number of bacteria in each sample of the 12 soil samples in the proximal group (CR) was about 115, and the number of bacteria in each soil sample of the 8 soil samples in the distal group (FBR) was about 92. The number of bacteria in the high-mercury soil near the mercury mining area was higher than that in the medium-high-mercury soil far from the mercury mining area.

At the family level, the distribution of fungi in 12 soil samples of the proximal group (CR) was shown in Fig. 1.7. In the proximal group (CR), the number of common fungi in 12 soil samples was 36, and the number of unique fungi in 12 soil samples was very different. Among them, the number of unique fungi in CMSO21 soil samples was 7, 6 unique fungi in MSO4 and GMSO302 soil samples, 5unique fungi in MSO3 soil samples, 4 unique fungi in MSO2 and CMSO24 soil samples, 3 unique fungi in GMSO303, GMSO304, CMSO23 soil samples. 1unique fungi in CMSO22 and GMSO301 soil samples, while 0 unique fungi in MSO1 soil samples. The distribution of fungi in 8 soil samples of the distal group (FBR) was shown in Fig. 1.8. The number of common fungi in the 8 soil samples of the distal group (FBR) was 16, and the number of unique fungi among the soil samples was very different. Among them, the number of unique fungi in GMSO503, GMSO501 and GMSO502 soil samples was 22,19 and 15, respectively, while 1unique fungi in GMSO500 soil samples. Among GMSO651, GMSO652, GMSO653 and GMSO654 soil samples, only GMSO654 soil samples had 6, GMSO651 and GMSO653 soil samples had 1, and GMSO652 soil samples had 0. From the distribution of fungi in all 20 soil samples, the number of fungi in each sample of 12 soil samples in the proximal group (CR) was about 39, and the number of fungi in each sample of 8 soil samples in the distal group (FBR) was about 22.

Distribution of bacteria and fungi at Genus level at different distances from mercury mining area

At the genus level, the bacterial distribution of 12 soil samples in the proximal group (CR) was shown in Fig. 1.9. The number of common bacteria in the 12 soil samples in the proximal group (CR) was 173, and the number of unique bacteria to each of the 12 soil samples was very different. Among them, the number of unique bacteria to the MSO1 soil sample was the highest, reaching 29. 1,4, and 1unique bacteria in MSO2, MSO3, and MSO4 soil samples respectively. 3,5,3, and 2 unique bacteria in CMSO21, CMSO22, CMSO23, and CMSO24 soil samples respectively. 1,1,0,1unique bacteria in GMSO301, GMSO302, GMSO303 and GMSO304 soil samples respectively. The bacterial distribution of 8 soil samples in the distal (FBR) group was shown in Fig. 2.0. The number of common bacteria in 8 soil samples in the distal group (FBR) was 101, and the number of unique bacteria to each soil sample varies greatly. Among them, the number of unique bacteria in GMSO651 soil samples was 62, and 12,14,1 unique bacteria in the GMSO652, GMSO653, and GMSO654 soil samples respectively. 4,6,3 and 3 unique bacteria in GMSO500, GMSO501, GMSO502 and GMSO503 soil samples respectively. According to the bacterial distribution of all 20 soil samples, the number of bacterial distribution in each sample of 12 soil samples in the proximal group (CR) was about 177, while the number of bacterial distribution in each sample of 8 soil samples in the distal group (FBR) was about 114, the results indicated that the number of bacterial distribution in high mercury soil near the mercury mining area was much higher than that in medium-high mercury soil far away from the mercury mining area.

At the genus level, the distribution of fungi in 12 soil samples of the proximal group (CR) was shown in Fig. 2.1. The number of common fungi in the 12 soil samples of the proximal group (CR) was 21, but there was a big difference in the number of unique fungi among the 12 soil samples. The number of unique fungi in MSO1, MSO2, MSO3 and MSO4 soil samples was 2,13,14,21, respectively. 22,5,12,16 unique fungi in CMSO21, CMSO22, CMSO23 and CMSO24 soil samples respectively. 6,10,11,12 unique fungi in GMSO301, GMSO302, GMSO303 and GMSO304 soil samples respectively. The distribution of fungi in 8 soil samples of the distal group (FBR) was shown in Fig. 2.2. The total number of fungi in the 8 soil samples of the distal group (FBR) was 12, but the number of unique fungi among the 8 soil samples varies greatly. Among them, the number of unique fungi in GMSO500, GMSO501, GMSO502, GMSO503 was 8,50,44,55, respectively, and 14,6,2,11 unique fungi in GMSO651, GMSO652, GMSO653, GMSO654 soil samples respectively. From the distribution of fungi in all 20 soil samples, the number of fungi each sample in the 12 soil samples of the proximal group (CR) was about 33, and the number of fungi per sample in the 8 soil samples of the distal group (FBR) was about 35, the results indicated that the number of fungi in the high-mercury soil near the mercury mining area was close to the number of fungi in the low-mercury soil far from the mercury mining area. And at the genus level, the total number of bacterial in each soil sample in both the proximal group and the distal group was higher than the total number of fungi.

The relative abundance of soil bacteria and fungi at the order level at different distances from mercury mining areas

At the order level, the relative abundance of bacteria in 20 soil samples was shown in Fig. 2.3.The bacteria with higher average relative abundance in MSO1, MSO2, MSO3 and MSO4 high mercury soil samples were Sphingomonadales(8.74%),Rhizobiales(8.38%), Sphingobacteriales (7.78%), Gp4 (norank_Acidobacteria_Gp4) (5.16%), Flavobacteriales(4.28%), Gp6(norank_Acidobacteria_Gp6)(4.14%), Burkholderiales(3.94%), Pseudomonadales (3.68%), Xanthomonadales (3.05%), Rhodospirillales (3.04%). The bacteria with higher average relative abundance in CMSO21, CMSO22, CMSO23 and CMSO24 were Sphingobacteriales(9.10%), Gp6(norank_Acidobacteria_Gp6)(5.14%), Sphingomonadales(4.96%),Anaerolineales(4.83%),Rhodospirillales(3.85%),Planctomycetales(3.77%), Rhizobiales (3.53%), Flavobacteriales (3.45%), Xanthomonadales (2.93%), Gp4(norank_Acidobacteria_Gp4)(2.78%). The bacteria with higher average relative abundance in CMSO301, CMSO302, CMSO303 and CMSO304 were Sphingomonadales(8.88%), Gp4(norank_Acidobacteria_Gp4)(8.83%),Sphingobacteriales(7.33%),Gp6(norank_Acidobacteria_Gp6)(7.12%),Rhodospirillales(4.38%),norank_Spartobacteria(4.10%),Planctomycetales(4.06%),Rhizobiales(3.82%),Xanthomonadales(3.52%),Actinomycetales(2.88%). The bacteria with higher average relative abundance in GMSO500, GMSO501, GMSO502 and GMSO503 were Sphingomonadales(11.31%), Sphingobacteriales(9.97%), Gp4(norank_Acidobacteria_Gp4)(6.90%), Gp6(norank_Acidobacteria_Gp6)(5.32%),Rhizobiales(4.30%),Anaerolineales(4.23%),Gemmatimonadales(4.21%),Rhodospirillales(3.35%),Planctomycetales(2.67%),Gp3(norank_Acidobacteria_Gp3)(2.67%),Actinomycetales(2.64%),Burkholderiales (2.50%). The bacteria with higher average relative abundance in GMSO651, GMSO652, GMSO653 and GMSO654 were Burkholderiales(20.01%),Bacteroidales(10.27%),Sphingomonadales(7.24%),Sphingobacteriales(4.60%), Gp1(norank_Acidobacteria_Gp1)(4.47%),Caulobacterales(4.39%),Rhizobiales(3.79%),Clostridiales (3.43%), Gp3( norank_ Acidobacteria_Gp3) (2.89%).

At the order level, the relative abundance of fungi in 20 soil samples was shown in Fig. 2.4. The fungi with higher average relative abundance in MSO1, MSO2, MSO3, and MSO4 were Agaricales(63.48%), Sebacinales(8.36%), Hypocreales(5.59%), Archaeorhizomycetales(2.98%), Chaetothyriales(2.67%), Capnodiales (2.59%), Mortierellales(2.10%), Helotiales(2.04%). The fungi with higher average relative abundance in CMSO21, CMSO22, CMSO23 and CMSO24 were Sebacinales(28.34%), Mortierellales(19.24%), Trechisporales(6.94%), Helotiales(2.77%),Hypocreales(2.70%), Pleosporales (2.67%), Lulworthiales(2.31%),Orbiliales(2.31%), Auriculariales(2.03%).The fungi with higher average relative abundance in CMSO301, CMSO302, CMSO303 and CMSO304 were Agaricales(65.13%), Archaeorhizomycetales(15.27%), Sebacinales(5.74%) and Chaetothyriales(2.13%). The fungi with higher average relative abundance in GMSO500, GMSO501, GMSO502, and GMSO503 were Agaricales(23.93%), Geoglossales(14.46%), Sebacinales(12.62%), Archaeorhizomycetales(7.42%),Corticiales(6.99%), Sordariales(4.04%), Pleosporales(3.21%),Chaetothyriales(2.911%), Onygenales(2.81%), Mortierellales(2.41%), Pezizales(2.25%), Russulales(2.16%).The fungi with higher average relative abundance in GMSO651, GMSO652, GMSO653 and GMSO654 were Helotiales(16.03%), Mortierellales(8.88%), Sebacinales (8.17%), Saccharomycetales (6.06%) ,Sordariales (5.54%). Hypocreales (4.85%), Chaetothyriales (4.64%), Pleosporales (4.38% ), Geoglossales (4.03%), Russulales (3.80%), Polyporales (3.65%), Archaeorhizomycetales (3.14%). Capnodiales (2.61%), Tremellales (2.50%), Agaricales (2.32%).

The relative abundance of bacteria and fungi at the family level at different distances from the mercury mining area.

At the family level, the relative abundance of bacteria in each soil sample was shown in Fig. 2.5. The bacteria with higher average relative abundance in MSO1,MSO2,MSO3,and MSO4 were Sphingomonadaceae(8.01%), Chitinophagaceae(6.57%),Gp4(norank_Acidobacteria_Gp4)(5.16%),Flavobacteriaceae(4.22%),Gp6(norank_ Acidobacteria_Gp6)(4.14%),Pseudomonadaceae(2.89%),Planctomycetaceae(2.85%),Enterobacteriaceae(2.63%), Xanthomonadaceae (2.12%), Comamonadaceae (2.02%). The bacteria with higher average relative abundance in CMSO21, CMSO22, CMSO23 and CMSO24 were Chitinophagaceae(7.71%), Gp6(norank_Acidobacteria_Gp6)(5.14%), Anaerolineaceae(4.83%), Sphingomonadaceae(4.74%),Planctomycetaceae(3.77%), Flavobacteriaceae(3.43%), Gp4 (norank_Acidobacteria_Gp4) (2.78%), Rhodospirillaceae(2.15%), Gemmatimonadaceae (2.12%).The bacteria with higher average relative abundance in CMSO301, CMSO302, CMSO303 and CMSO304 were Gp4 (norank_Acidobacteria_Gp4) (8.83%), Sphingomonadaceae (8.43%), Gp6 (norank_Acidobacteria_Gp6)(7.12%), Chitinophagaceae(6.97%), Norank _Spartobacteria(4.10%), Planctomycetaceae(4.06%), Rhodospirillaceae(3.04%). The bacteria with higher average relative abundance in GMSO500, GMSO501, GMSO502 and GMSO503 were Sphingomonadaceae(10.93%), Chitinophagaceae (9.42%) and Gp4(norank_Acidobacteria_Gp4)(6.90%), Gp6(norank_Acidobacteria_Gp6)(5.32%),Anaerolineaceae (4.23%), Gemmatimonadaceae (4.21%), Planctomycetaceae (2.67%), Gp3 (norank_Acidobacteria_Gp3) (2.66%). The bacteria with average relative abundance in GMSO651, GMSO652, GMSO653 and GMSO654 were Burkholderiaceae(13.81%), Porphyromonadaceae(8.40%), Sphingomonadaceae(7.16%),Oxalobacteraceae (4.75%), Gp1 (norank_ Acidobacteria_Gp1 ) (4.47%),Caulobacterales(4.37%),Chitinophagaceae(4.26%),Gp3(norank_Acidobacteria_Gp3)(2.89%), Gemmatimonadaceae (2.16%).

At the family level, the relative abundance of bacteria in each soil sample was shown in Fig. 2.5. The bacteria with higher average relative abundance in MSO1, MSO2, MSO3, and MSO4 were Sphingomonadaceae(8.01%), Chitinophagaceae(6.57%),Gp4(norank_Acidobacteria_Gp4)(5.16%),Flavobacteriaceae(4.22%),Gp6(norank_ Acidobacteria_Gp6)(4.14%),Pseudomonadaceae(2.89%),Planctomycetaceae(2.85%),Enterobacteriaceae(2.63%), Xanthomonadaceae (2.12%). Comamonadaceae (2.02%).The bacteria with higher average relative abundance in CMSO21, CMSO22, CMSO23 and CMSO24 were Chitinophagaceae(7.71%), Gp6(norank_Acidobacteria_Gp6)(5.14%), Anaerolineaceae(4.83%),Sphingomonadaceae(4.74%),Planctomycetaceae(3.77%),Flavobacteriaceae(3.43%),Gp4 (norank_Acidobacteria_Gp4) (2.78%), Rhodospirillaceae (2.15%), Gemmatimonadaceae (2.12%). The bacteria with higher average relative abundance in CMSO301, CMSO302, CMSO303 and CMSO304 were Gp4 (norank_ Acidobacteria_Gp4 ) (8.83%),Sphingomonadaceae(8.43%),Gp6(norank_Acidobacteria_Gp6)(7.12%),Chitinophagaceae (6.97%),Norank_Spartobacteria(4.10%),Planctomycetaceae(4.06%), Rhodospirillaceae(3.04%).The bacteria with higher relative abundance in GMSO500,GMSO501,GMSO502 and GMSO503 were Sphingomonadaceae(10.93%), Chitinophagaceae(9.42%),Gp4(norank_Acidobacteria_Gp4)(6.90%),Gp6(norank_Acidobacteria_Gp6)(5.32%), Anaerolineaceae(4.23%),Gemmatimonadaceae(4.21%),Planctomycetaceae(2.67%),Gp3(norank_Acidobacteria_Gp3)(2.66%). The bacteria with higher average relative abundance in GMSO651, GMSO652, GMSO653 and GMSO654 were Burkholderiaceae(13.81%),Porphyromonadaceae(8.40%),Sphingomonadaceae(7.16%),Oxalobacteraceae(4.75%),Gp1(norank_Acidobacteria_Gp1)(4.47%),Caulobacterales(4.37%),Chitinophagaceae(4.26%),Gp3(norank_Acidobacteria_Gp3)(2.89%),Gemmatimonadaceae (2.16%).

At the family level, the relative abundance of fungi in 20 soil samples was shown in Fig. 2.6. The fungi with higher average relative abundance in MSO1, MSO2, MSO3, and MSO4 were Hygrophoraceae (58.45%), Clavariaceae (4.03%), Nectriaceae (3.08%), Archaeorhizomycetace (2.98%). Herpotrichiellaceae (2.26%), Mortierellaceae (2.09%), Helotiaceae (1.91%), Sebacinaceae (1.82%). The fungi with higher average relative abundance in CMSO21, CMSO22, CMSO23 and CMSO24 were Sebacinaceae (28.02%), Mortierellaceae (19.24%), Hygrophoraceae (6.94%), Nectriaceae (1.22%), Cucurbitariaceae (1.21%). Hyaloscyphaceae (1.09%).The fungi with higher average relative abundance in CMSO301, CMSO302, CMSO303 and CMSO304 were Hygrophoraceae(26.34%), Tricholomataceae(21.18%), Clavariaceae(17.10%),Archaeorhizomycetace(15.27%),Serendipitaceae (4.85%), Herpotrichiellaceae (1.98%), Nectriaceae (1.28%), Mortierellaceae(1.18%). The fungi with higher average relative abundance in GMSO500, GMSO501, GMSO502 and GMSO503 were Geoglossaceae(14.37%), Clavariaceae(12.94%), Archaeorhizomycetace(7.42%),Corticiaceae (6.99%). Serendipitaceae (6.30%), Sebacinaceae (5.58%), Entolomataceae (5.14%), Hygrophoraceae (4.55%), Lasiosphaeriaceae (3.09%), Mortierellaceae (2.41%), Pyronemataceae (2.23%), Russulaceae (2.14%).The fungi with higher average relative abundance in GMSO651, GMSO652, GMSO653 and GMSO654 were Helotiaceae (10.16%), Mortierellaceae (8.88%), Sebacinaceae (6.17%), Nectriaceae (4.15%), Geoglossaceae (4.03%). Lasiosphaeriaceae (3.85%), Russulaceae (3.80%), Saccharomycetaceae (3.48%), Archaeorhizomycetace (3.14%), Mycosphaerellaceae (2.52%), Podoscyphaceae (2.51%). Trichocomaceae (2.20%).

The relative abundance of bacteria and fungi at the genus level in soils at different distances from mercury mining areas.

At the genus level, the relative abundance of bacteria in 20 soil samples was shown in Fig. 2.7. The bacteria with higher average relative abundance in MSO1, MSO2, MSO3, and MSO4 were Gp6 (norank_Acidobacteria_Gp6) (4.14%), Flavobacterium(4.12%),Sphingomonas(3.77%),Gp4(norank_Acidobacteria_Gp4)(3.49%),Pseudomonas(2.72%),Buttiauxella(2.44%),Saccharibacteria_genera_incertae_sedis(1.77%),Gp3(norank_Acidobacteria_Gp3)(1.42%),Lacibacterium (1.34%),Terrimonas (1.28%), Rhizobium (1.15% ), Gp7 (1.08%), Gemmatimonas (1.06%). The bacteria with higher average relative abundance in CMSO21, CMSO22, CMSO23 and CMSO24 were Acidobacteria Gp6(5.14%), Flavobacterium (3.37%), Sphingomonas (3.30%), Gemmatimonas (3.19%), Lacibacterium (2.12%), Gp4 (1.78%), Gp3 (1.71%), Subdivision3_genera_incertae_sedis (1.55%), Geobacter (1.20%), Lysobacter (1.03%). The bacteria with higher average relative abundance in CMSO301, CMSO302, CMSO303 and CMSO304 were Gp4(7.20%), Gp6(7.12%), Sphingomonas(4.11%),Spartobacteria_genera_incertae_sedis(3.82%),Lacibacterium(2.88%),Subdivision3_genera_incertae _ sedis (2.49%), WPS-1_genera_incertae_sedis (2.13%), Terrimona(1.43%), Lysobacter (1.36%), Gp3(1.32%), Gaiella (1.15%), Pirellula (1.05%). The bacteria with higher average relative abundance in GMSO500, GMSO501, GMSO502 and GMSO503 were Gp6 (5.32%), Sphingomonas (4.70%), Gp4 (4.40%), Gemmatimonas (4.21% ), Gp3 (2.37%) and Streptophyta(2.24%),Spartobacteria_genera_incertae_sedis(1.84%),Lacibacterium(1.69%),WPS-1_genera_incertae_sedis(1.69%), Pseudomonas (1.60%), Subdivision3_genera_incertae_sedis (1.30%), Gp7 (1.10%).The bacteria with higher average relative abundance of GMSO651, GMSO652, GMSO653 and GMSO654 were Ralstonia (13.49%), Herbaspirillum (4.43%), Phenylobacterium (4.33%), Gp1 (2.92%), Gp3 (2.54%) and Gemmatimonas(2.16%). Gp2 (1.80%), Lactobacillus (1.80%), Sphingomonas (1.64% ), Acinetobacter(1.33%), Spartobacteria_genera_incertae_sedis (1.30%), Gp6 (1.04%).

At the genus level, the relative abundance of fungi in each soil sample was shown in Fig. 2.8. The fungi with higher average relative abundance in MSO1, MSO2, MSO3, and MSO4 were Hygrocybe(58.45%), Clavaria(4.03%), Archaeorhizomyces(2.98%), Fusarium(2.88%), Mortierella(2.09%), Brachysporium(1.73%), Exophiala(1.57%), Neodevriesia (1.43%), Serendipita (1.26%), Tetracladium (1.06%).The fungi with higher average relative abundance in CMSO21, CMSO22, CMSO23 and CMSO24 were Helvellosebacina (27.85%), Mortierella (19.24%), Trechispora (6.94%), Oliveonia (1.90%), Pyrenochaetopsis(1.14%).The fungi with higher average relative abundance in CMSO301, CMSO302, CMSO303 and CMSO304 were Hygrocybe (26.34%), Dermoloma(21.04%), Clavaria(17.10%), Archaeorhizomyces (15.27%), Serendipita (4.85%), Mortierella(1.13% ). The fungi with higher average relative abundance in GMSO500, GMSO501, GMSO502 and GMSO503 were Trichoglossum(13.9%), Clavaria(12.94%), Archaeorhizomyces(7.42%), Serendipita(6.30%), Entoloma(5.06%), Hygrocybe(4.55%), Sebacina(3.76%), Mortierella(2.07%), Cercophora(1.84%), Knufia (1.37%), Russula (1.78%).The fungi with higher average relative abundances in GMSO651, GMSO652, GMSO653 and GMSO654 were Collophora(9.74%), Mortierella(8.88% ), Sebacina(3.84%), Archaeorhizomyces(3.14%),Kazachstania (2.97%), Trichoglossum (2.73%), Cotylidia (2.51%), Lactifluus (2.29%), Echria (2.21%), Knufia (2.10%), Sphaerulina (1.78%), Serendipita (1.63%), Ilyonectria (1.54%), Russula (1.51%).

Discussion and conclusions

Mercury is one of the most common heavy metal pollutants in the environment. Soil mercury pollution is often closely related to the mining of surrounding mercury-related metal mines and the smelting of mercury-related metals. Once soil is polluted by mercury, it will be transferred to plant roots, stems, leaves and other parts through plant enrichment. When the soil mercury content reaches a certain level, it will seriously affect the normal growth and development of plants, and will further transfer to other animals through the enrichment of the food chain, which will cause great damage to animal nerve function^20,21. On the other hand, when the soil is polluted by heavy metals, it will have adverse effects on the soil ecosystem, especially on soil microorganisms and soil animals²². Previous studies have confirmed that the concentration of Cd, Zn, Pb and Cu in soil has a strong correlation with soil microbial biomass²³, and different microbial groups have different tolerance to different heavy metals²⁴. The reason why heavy metals have adverse effects on microbial diversity may be that high concentrations of heavy metals have a certain restrictive effect on microbial cell metabolism and other functions, which ultimately leads to a decrease in microbial diversity²⁵.

In this work, the results of bacterial and fungal diversity in soils with different mercury contents at different distances from mercury mining areas showed that the number of bacteria and fungi in 20 soil samples at different distances from mercury mining areas increased significantly according to the order of order, family and genus, and the increase of bacterial number was much higher than that of fungal number, especially at the family level and genus level. The number of bacteria and fungi unique to each of the 20 soil samples showed a significant increase trend. This result showed that even in a certain mercury stress environment, the number of bacteria and fungi in the soil around the mercury mining area increases with the decrease of the classification level, which was not affected by soil mercury and other factors. In this study, there were some differences in the number of unique bacteria and fungi to the four repeated soil samples at different points in the same distance from the mercury mining area, and even the number of unique bacteria and fungi to individual samples in the four repeated soil samples of the same group was abnormal. These results indicated that in the outdoor natural environment, the diversity distribution of bacteria and fungi in the soil at different points in the same range was not affected by the internal factors of the soil. For example, when the mercury content in the soil at different points in the same range was basically close, the diversity of bacteria and fungi was affected by many factors, resulting in the difference in the number of bacteria and fungi unique to individual soil samples²⁶. It also showed that there was a certain heterogeneity in the soil around the mercury mining area. Therefore, it can be speculated that heavy metal mercury pollution will lead to a decline in soil microbial diversity to a certain extent, but the relationship between the two was not a simple linear relationship, but also affected by other factors within the soil.

The results of this study showed that the number of bacteria in 2–20 m high mercury soil samples from mercury mining area was much higher than that in 30–650 m medium-high mercury soil samples from mercury mining area at any classification level of order, family and genus. The distribution of the number of fungi was very similar to the distribution of the number of bacteria, that is, the number of bacteria and fungi in the soil with high mercury concentration was higher than that in the soil with high mercury concentration at any level of order, family and genus. This result was inconsistent with the research results of Shan et al.²⁷ and Li et al.⁹. The main reason was that the soil mercury concentration was very different. The soil mercury concentration range in this work is 45.42-175.02 mg/kg. The second reason was that the experimental environment was different. Previous studies belong to the soil environment of artificial simulation laboratory. This study was the natural soil environment of mercury mining area, various bacteria in soil samples, and fungi reach an equilibrium state after a long period of comprehensive influence of various factors inside the soil. From the perspective of resistance to heavy metal mercury, the real existence of these bacteria and fungi was a good adaptation to the soil 's own environment, indicating that these bacteria and fungi were highly adapted to the mercury stress environment for a long time. The reason for this result may be related to the different types of soil. Maliszewska et al.²⁸found that the compounds of Cu and Hg showed the strongest inhibitory effect on microbial proliferation, and the order of toxicity of HgCl₂ to microorganisms was bacteria > fungi > actinomycetes. The results showed that the inhibitory effect of mercury on microbial proliferation was fluctuating, and the inhibition of microorganisms decreased with the prolongation of culture time²⁷.In this study, the soil around the mercury mining area has been subjected to high concentration mercury pollution stress for a long time. The increase in the number of bacteria and fungi under high concentration mercury environment may be the best evidence that the inhibition of bacteria and fungi decreases with time.

The community structure of bacteria, fungi and actinomycetes in soil is easily affected by environmental variables around the soil, such as nutrient cycling and heavy metal concentration changes^29,30. In this work, the effects of mercury concentration on the community structure of bacteria and fungi at the order, family and genus levels were analyzed. It was found that mercury had a certain effect on the community structure of bacteria and fungi, and the tolerance of different bacteria and fungi to mercury pollution was also very different, which was consistent with the results of Prasad³¹.In terms of changes in bacterial community structure, at the level of order, family and genus, the bacteria with high relative abundance in both high mercury (119.51-175.02 mg/kg ) soil and medium high mercury (45.42–72.31 mg/kg ) soil changed. At the order level, the bacteria with high relative abundance were Sphingomonadales and Sphingobacteriales. The relative abundance remained at about 10%, which was the dominant bacteria in the soil. This fully demonstrated that Sphingomonadales and Sphingobacteriales were very resistant to mercury pollution. At the family level, the bacteria with high relative abundance were Sphingomonadaceae and Chitinophagaceae, which were the dominant bacteria in the soil. The bacteria with high relative abundance at the genus level were Gp6(norank_Acidobacteria_Gp6),Sphingomonas,Gp4 (norank_Acidobacteria_Gp4).Similarly, in terms of the effect of different concentrations of mercury on the fungal community structure, at the order level, the fungi with the highest relative abundance in both high-mercury soil and medium-high mercury soil were Agaricales and Sebacinales. At the level of family, the fungi with the highest relative abundance in both high-mercury soil and medium-high mercury soil were Sebacinaceae and Hygrophoraceae. At the genus level, Hygrocybe and Sebacina had the highest relative abundance, which were the dominant fungi in soil and showed strong mercury tolerance. Regardless of the classification level, bacteria and fungi with high relative abundance as the dominant bacteria in the soil were the performance of long-term adaptation to high mercury and medium-high mercury soil environment, indicating that these bacteria have strong tolerance to high concentration mercury stress. This was similar to the conclusion of Feris³² and Gillan et al³³. At the same time, at any level of order, family and genus, the types of bacteria and fungi with high relative abundance in different positions of mercury mining area were quite different, which was closely related to the heterogeneity of many factors in the internal environment of soil ²².

There is a complex mutual utilization relationship between plants and rhizosphere microorganisms. Microorganisms can usually affect the photosynthesis, physiological and biochemical metabolic processes of plants, and then have adverse effects on plant growth and root function. Therefore, it was of great significance to study the synergistic survival relationship between rhizosphere microorganisms and plants under heavy metal stress, make full use of soil microorganisms to effectively protect plants from heavy metal stress, and improve the survival of plants in heavy metal stress environment. Fully exploring the correlation between rhizosphere soil flora and plant growth provides an effective scientific reference for further exploring the mechanism of synergistic mercury enrichment between rhizosphere microorganisms and plants.

From the perspective of the effects of mercury stress on bacterial and fungal diversity at different distances from mercury mining areas, at the level of order, family and genus, the number of bacteria and fungi in 2-20m high mercury soil samples from mercury mining areas was significantly higher than that in 30-650m medium high mercury soil samples from mercury mining areas. At the same level, there were also great differences in the unique bacteria and fungi of soil samples at different points at the same distance from the mercury mining area. From the point of view of the influence of mercury stress on the community structure of bacteria and fungi at different distances from the mercury mining area, at the order level, the dominant bacterial species in the high-mercury soil or medium-high-mercury soil samples at 2-650 m from the mercury mining area were Sphingomonadales and Sphingobacteriales, and the dominant fungi were Agaricales and Sebacinales ; at the family level, the dominant bacteria were Sphingomonadaceae and Chitinophagaceae, and the dominant fungi were Sebacinaceae and Hygrophoraceae. At the genus level, the dominant bacteria were Gp6 ( norank_Acidobacteria _Gp6), Sphingomonas, Gp4(norank_Acidobacteria_Gp4 ), and the dominant fungi were Hygrocybe and Sebacina. Moreover, there were great differences in the relatively high abundance of bacteria and fungi in the five groups of soil samples at any level of order, family and genus except for the common dominant bacteria.

Acknowledgements

Thanks to the Science and Technology Plan Project of Guizhou Province (Qiankehe foundation-ZK [ 2021 ] General 229) for supporting this project with funds.

Author information

Authors and Affiliations

School of Management Science and Engineering , Guizhou University of Finance and Economics, Guiyang, 550025, China

Jianxiong Du¹,

School of Foreign Languages , Guizhou Normal University, Guiyang, 550025, China

Yuxiang REN²

Institute of Soil and Environment Bioremediation in Karst habitats, Guizhou Normal College, Guiyang 550018, China

Jianfeng Li³, Shuqing Zhang³,

College of Public Management, Guizhou University of Finance and Economics, Guiyang, 550025, China

Huiqiong Huang¹, Jie Liu¹

Author contributions

J.D. and J.L. performed experiments. J.D.and Y.R. wrote the manuscript. S.Z. searched and verified the formula of the manuscript. Y.R. polished the manuscript, H.H. and J.L. carried out data collection on the device in the manuscript,all authors reviewed the manuscript.

Corresponding author

Correspondence to JianFeng Li.

Competing interests

The authors declare no competing interests.

Data availability statement

The raw sequence data reported in this paper have been deposited in the Genome Sequence Archive (Genomics, Proteomics & Bioinformatics 2021) in National Genomics Data Center (Nucleic Acids Res 2022), China National Center for Bioinformation / Beijing Institute of Genomics, Chinese Academy of Sciences (GSA: CRA011955) that are publicly accessible at https://ngdc.cncb.ac.cn/gsa.

Please cite the following required publications:

[1] The Genome Sequence Archive Family: Toward Explosive Data Growth and Diverse Data Types. Genomics, Proteomics & Bioinformatics 2021, 19(4):578-583. https://doi.org/10.1016/j.gpb.2021.08.001 [PMID=34400360]

[2] Database Resources of the National Genomics Data Center, China National Center for Bioinformation in 2022. Nucleic Acids Res 2022, 50(D1):D27-D38. https://doi.org/10.1093/nar/gkab951 [PMID=34718731]

Hui, T.,Ying, C., Yanwei, L., et al. Bioaccumulation of mercury in plants collected from Wanshan mercury mining areas in Guizhou province [J].Environmental Chemistry, 2022,41(12):4047–4056.(in Chinese with English abstract)
Jiating, Z., Yunyun, L.,Yuxi,G,. et al. Study of mercury resistant wild plants growing in the mercury mine area of Wanshan district,Guizhou Province[J].Asian Journal of Ecotoxicology,2014,9(5):881–887.(in Chinese with English abstract)
Xiaoli, Q,.Tolerant plants and their accumulation mechanism of mercury in typical mercury mining areas[D].Guizhou University,2018.(in Chinese with English abstract)
Jianxiong,D., Yuxiang, R., Jianfeng, L,.et al. Effects of Hg stress on growth characteristics and Hg enrichment ability of 4 turfgrass, forage varieties seedlings[J].Southwest China Journal of Agricultural Sciences,2021,34(09):1969–1976.DOI:10.16213/j.cnki.scjas.2021.9.021.(in Chinese with English abstract)
Shijia, M., Zhi, Y., Hongyan, L., et al.Effects of low-temperature pyrolysis of mercury-contaminated soil on the accumulation of total mercury and methylmercury in vegetables[J/OL].Earth and Environment,1–8[2023-05-27].https://doi.org/10.14050/j.cnki.1672-9250.2023.51.010.(in Chinese with English abstract)
Bing,H., Xiao-e, Y., Youzhang,W,. Remediation of lead-contaminated soil[J]. Guangdong Trace Elements Science, 2001, 8(9):12–17.(in Chinese with English abstract)
Subires-Munoz, J., Garcia-Rubio, A., Vereda-Alonso, C., et al. Feasibility study of the use of different extractant agents in the remediation of a mercury contaminated soil from almaden[J]. Sep Sci Technol, 2011, 79: 151–156.
Rufus, L. Chaney., Malik, M., Yin, M. L., et al. Phytoremendiation of soilmetals[J]. Current Opinionin Biotechnology, 1997, 8(3): 279–284.
Mei,L.,Yanlin,H.,Guangjie,P. Microbial populations and bacterial physiological groups in purple soils polluted by mercury[J].Journal of Agro-Environment Science,2004,(04):668–673.(in Chinese with English abstract)
Youdan,L.. Screening and perfoemance evaluation of mercury-rich plant[D].Beijing University of Chemical Technology,2015.(in Chinese with English abstract)
Xiaorong,X,. The Selection of Hg-tolerance plants and the study of the tolerance mechanisms in the WanShan Mine[D].Guizhou Normal University,2008.(in Chinese with English abstract)
Zhenjing,L.Biological effects of soil mercury on plant growth and microbial community[D].Beijing University of Chemical Technology,2019. DOI:10.26939/d.cnki.gbhgu.2019.000461.(in Chinese with English abstract)
Taotao,F,.Study on the screening, identification and mercury conversion mechanism of mercury tolerant strains[D].Lanzhou Jiaotong University,2019. DOI:10.27205/d.cnki.gltec.2019.000826.(in Chinese with English abstract)
Harris,H.J.,Vallaeys,T.,Garnier,Z.E.,et al. Effects of mercury on soil microbial communities in tropical soils of French Guyana[J].Applied Soil Ecology,2009,41(1):59–68.
Xie, X., Min, L., Aili, M. A., et al. Effects of contamination of single and combined cadmium and mercury on the soil microbial community structural diversity and functional diversity[J]. Chinese Journal of Geochemistry, 2011, 30(3):366–374.(in Chinese with English abstract)
Liao, M., Zhang, H., Yu, S., et al. Effects of cadmium and mercury alone and in combination on the soil microbial community structural diversity[M]// Molecular Environmental Soil Science at the Interfaces in the Earth’s Critical Zone. Springer Berlin Heidelberg, 2010:337–341.
Frey, B., Rieder, S.R. Response of forest soil bacterial communities to mercury chloride application[J]. Soil Biology & Biochemistry, 2013, 65(5):329–337.
Yurong,L., Jianjun,W., Yuanming, Z., et al. Patterns of bacterial diversity along a long-term mercury-contaminated gradient in the paddy soils[J]. Microbial Ecology, 2014,68(3):575–583.
Frossard, A., Hartmann, M., Frey, B. Tolerance of the forest soil microbiome to increasing mercury concentrations[J]. Soil Biology & Biochemistry, 2017, 105:162–176.
Yu,Z.,Hai,L., Yingbo,D.,et al.Enrichment research of several aquatic plants on arsenic and mercury combined pollution in water[J].Metal World,2018,(4):15–19.(in Chinese with English abstract)
Fangqun,G., Bin,G.,Jianguo,W.,et al. Accumulation of mercury in rice grown in pot experimental soils and its impact factors[J].Journal of Ecology and Rural Environment,2018,34(7):630–635.(in Chinese with English abstract)
Lei,W. Research on the abiotic methylation of mercury and its effect on the microbial community in mercury-contaminated soil[D].ChongqingUniversity,2017.(in Chinese with English abstract)
Xiuli,W., Jianmin,X., Huaiying,Y., et al. Effects of Cu,Zn, Cd and Pb compound contamination on soil microbial community[J]. Acta Scientiae Circumstantiae, 2003, 23(1):22–27.(in Chinese with English abstract)
Jun,Z., Xin,Z., Chao Lei,C., et al. Diversity change of microbial communities responding to zinc and arsenic pollution in a river of northeastern China[J]. Journal of Zhejiang University-Science B, 2014,15(7):670–680.(in Chinese with English abstract)
Huaqun,Y. Development and Application of the microarray technology during the research on the structure and function of microbial communities for acid mine drainages from the copper mines[D]. Central South University, 2007.
Feris,K.,Ramsey,P., Frazar,C.,et al. Differences in hyporheic-zone microbial community structure along a heavy-metal contamination gradient[J]. Applied and Environmental Microbiology, 2003,69(9):5563–5573.
Ping,Sh., Zhenwei,W., Gaofei,G. Effect of exogenous mercury addition on soil microbial populations [J]. Journal of Anhui Agricultural University,2016,43(2):248–251.
Maliszewska,W. The influence of various heavy metal compounds on the development and activity of soil microorganism[J].Environmental Pollution, 1985, 37(3):195–215.
Mbuthia, L.W., Acosta,M. V., DeBruyn, J., et al. Long term tillage, cover crop, and fertilization effects on microbial community structure, activity: implications for soil quality[J]. Soil Biology and Biochemistry, 2015,89:24–34.
Aždajińc, M., Yumvihoze, E., Blais, J.M., et al. The effect of legacy gold mining on methyl-mercury cycling and microbial community structure in northern freshwater lakes[J]. Environmental Science:Processes & Impacts, 2021,23(8):1220–1230.
Prasad, M.N.V. Metals in the Environment:Analysis by Biodiversity[M]. Metals in the Environment:Analysis by Biodiversity, 2001.
Feris,K., Ramsey, P., Frazar, C., et al. Differences in hyporheic-zone microbial community structure along a heavy-metal contamination gradient[J]. Applied and Environmental Microbiology, 2003,69(9):5563–5573.
Gillan, D. C., Danis, B., Pernet,P.,et al. Structure of sediment associated microbial communities along a heavy-metal contamination gradient in the marine environment[J]. Applied and Environmental Microbiology, 2005,71(2):679–690.

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The Study on the effect of mercury pollution on soil microorganisms around mercury mining area

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Journal Publication

Version 1

Abstract

Figures

Introduction

Methods

Sample collection

DNA extraction

16S rRNA gene amplification by PCR

16S gene library construction,quantification,and sequencing

Sequence processing,OTU clustering,Representative tags alignment and Biological classification

Results

Distribution of bacteria and fungi at Genus level at different distances from mercury mining area

Discussion and conclusions

Conclusion

Declarations

References

Additional Declarations

Status:

Journal Publication

Version 1